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pymcr/tests/test_svd_simplisma.py

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+import numpy as np
+import random
+import matplotlib.pyplot as plt
+from pymcr.simplisma import svd, simplisma
+
+#generate some sample data D
+#create random normalised gaussian functions
+
+#Number of Spectral Components
+nPure = 5
+#maximum number of components for SVD
+nSVD = 15
+#Allowed Noise Percentage
+noise = 5	
+#manual
+manual = False
+
+x0 = np.zeros(nPure)
+sigma = np.zeros(nPure)
+for i in range(nPure):
+	x0[i] = random.uniform(-100, 100)
+	sigma[i] = random.uniform(3, 25)
+
+x = np.linspace(start = -120, stop = 120, num = 2000)
+
+gx = np.zeros((len(x),nPure))
+plt.subplot(5, 1, 1)
+plt.subplots_adjust(left=0.1, bottom=0.075, right=0.95, top=0.9, wspace=0.2, hspace=0.5)
+
+for i in range(nPure):
+	gx[:,i] = np.exp(-(x-x0[i])**2/(2*sigma[i]**2))/np.sqrt(2*np.pi*sigma[i]**2)
+	plt.plot(gx[:,i])
+	plt.title('Real Components')
+
+#create D with random normalised linear combination of gaussian functions
+nspec = 200
+D = np.zeros((len(x), nspec))
+idx = list(range(nPure))
+
+C_r = np.zeros((nspec, nPure))
+
+for i in range(nspec):
+	randj = np.zeros(nPure)
+	random.shuffle(idx)
+	for j in range(nPure):
+		if j < nPure-1:
+			randj[j] = random.uniform(0, 1-np.sum(randj))
+			D[:,i] = D[:,i]+(gx[:,idx[j]]*randj[j])
+			C_r[i,j] = randj[j]
+		elif j == nPure-1:
+			randj[j] = 1-np.sum(randj)
+			D[:,i] = D[:,i]+(gx[:,idx[j]]*randj[j])
+			C_r[i,j] = randj[j]
+
+#run SVD
+eigens, explained_variance_ratio = svd.svd(D, nSVD)
+nPure = np.int(input('Number of Principle Components for SIMPLISMA :'))
+
+#Run Simplisma
+S, C_u, _, _ = simplisma.pure(D, nPure, noise, False)
+
+#Run Simplisma with constrained LCF
+S, C_u, C_c, LOF = simplisma.pure(D, nPure, noise, True)
+
+
+
+